Exploring the Potential of Mapping Cropping Patterns on Smallholder Scale Croplands Using Sentinel-1 SAR Data

Juliana USEYA; CHEN Shengbo

doi:10.1007/s11769-019-1060-0

Volume 29 Issue 4

Aug. 2019

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Juliana USEYA, CHEN Shengbo. Exploring the Potential of Mapping Cropping Patterns on Smallholder Scale Croplands Using Sentinel-1 SAR Data[J]. Chinese Geographical Science, 2019, 20(4): 626-639. doi: 10.1007/s11769-019-1060-0

Citation:

Juliana USEYA, CHEN Shengbo. Exploring the Potential of Mapping Cropping Patterns on Smallholder Scale Croplands Using Sentinel-1 SAR Data[J]. Chinese Geographical Science, 2019, 20(4): 626-639. doi: 10.1007/s11769-019-1060-0

Exploring the Potential of Mapping Cropping Patterns on Smallholder Scale Croplands Using Sentinel-1 SAR Data

doi: 10.1007/s11769-019-1060-0

College of GeoExploration Science and Information Engineering, Jilin University, Changchun 130026, China

Funds: Under the auspices of Fundamental Research Funds for the Central Universities, China (No. 2017TD-26), the Plan for Changbai Mountain Scholars of Jilin Province, China (No. JJLZ[2015]54)

More Information

Corresponding author: Juliana USEYA.E-mail:julieuseya@yahoo.co.uk;CHEN Shengbo.E-mail:chensb@jlu.edu.cn
Received Date: 2018-12-03
Rev Recd Date: 2018-08-08
Publish Date: 2019-08-01

Abstract

It is of paramount importance to have sustainable agriculture since agriculture is the backbone of many nations' economic development. Majority of agricultural professionals rarely capture the cropping patterns necessary to promote Good Agricultural Practises. Objective of this research is to explore the potential of mapping cropping patterns occurring on different field parcels on small-scale farmlands in Zimbabwe. The first study location under investigation are the International Maize and Wheat Improvement Center (CIMMYT) research station and a few neighboring fields, the second is Middle Sabi Estate. Fourier time series modeling was implemented to determine the trends befalling on the two study sites. Results reveal that Sentinel-1 synthetic aperture radar (SAR) time series allow detection of subtle changes that occur to the crops and fields respectively, hence can be utilized to detect cropping patterns on small-scale farmlands. Discrimination of the main crops (maize and soybean) grown at CIMMYT was possible, and crop rotation was synthesized where sowing starts in November. A single cropping of early and late crops was observed, there were no winter crops planted during the investigation period. At Middle Sabi Estate, single cropping on perennial sugarcane fields and triple cropping of fields growing leafy vegetables, tomatoes and onions were observed. Classification of stacked images was used to derive the crop rotation maps representing what is practised at the farming lands. Random forest classification of the multi-temporal image stacks achieved overall accuracies of 99% and 95% on the respective study sites. In conclusion, Sentinel-1 time series can be implemented effectively to map the cropping patterns and crop rotations occurring on small-scale farming land. We recommend the use of Sentinel-1 SAR multi-temporal data to spatially explicitly map cropping patterns of single-, double- and triple-cropping systems on both small-scale and large-scale farming areas to ensure food security.
- cropping patterns,
- polarized backscatter,
- time series,
- Sentinel-1 SAR,
- Zimbabwe

References

[1]	Ban Y, Howarth P J, 1999. Multitemporal ERS-1 SAR data for crop classification:a sequential-masking approach. Canadian Journal of Remote Sensing, 25(5):438-447. doi: 10.1080/07038992.1999.10874743
[2]	Bargiel D, 2017. A new method for crop classification combining time series of radar images and crop phenology information. Remote Sensing of Environment, 198:369-383. doi: 10.1016/j.rse.2017.06.022
[3]	Bégué A, Arvor D, Bellon B et al., 2018. Remote sensing and cropping practices:a review. Remote Sensing, 10(1):99. doi: 10.3390/rs10010099
[4]	Bharati P, De U K, Pal M, 2015. A modified diversity index and its application to crop diversity in Assam, India. AIP Conference Proceedings, 1643(1):19-29. doi: 10.1063/1.4907421
[5]	Breiman L, 2001. Random forest. Machine Learning, 45(1):5-32. doi: 10.1109/ACCESS.2019.2912807.
[6]	Chamundeeswari V V, Singh D, Singh K, 2007. Unsupervised land cover classification of SAR images by contour tracing. In:Proceedings of 2007 IEEE International Geoscience and Remote Sensing Symposium. Barcelona, Spain:IEEE, 547-550. doi: 10.1109/IGARSS.2007.4422852
[7]	CIMMYT, 2016. Our work. http://www.cimmyt.org/our-work/. Cited 22 March 2018.
[8]	Crawford M M, Ricard M R, 1998. Hierarchical classification of SAR data using a markov random field model. In:Proceedings of 1998 IEEE Southwest Symposium on Image Analysis and Interpretation. Tucson, AZ, USA:IEEE. doi: 10.1109/IAI.1998.666864
[9]	de Oliveira A T C, de Oliveira L T, de Carvalho L M T et al., 2009. Separabilities of forest types in amplitude-phase space of multi-temporal MODIS NDVI. In:Proceedings of the Anais 14th Simpósio Brasileiro de Sensoriamento Remoto. Natal, Brasil:INPE, 7.
[10]	Dimov D, Kuhn J, Conrad C, 2016. Assessment of cropping sys-tem diversity in the fergana valley through image fusion of landsat 8 and sentinel-1. Proceedings of ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Prague, Czech Republic:ISPRS, 173-180. doi: 10.5194/isprsannals-Ⅲ-7-173-2016
[11]	Doukkali F, 2017. Clustering using K-means algorithm. Towards Data Science. https://towardsdatascience.com/clustering-using-k-means-algorithm-81da00f156f6?gi=c1eec7743ec0. Cited 20 March 2018
[12]	Dzirutwe M, 2015. Zimbabwe takes tobacco road to agriculture recovery. https://www.yahoo.com/news/zimbabwe-takes-to-bacco-road-agriculture-recovery-082633369-business.html. Cited 21 March 2018
[13]	Foody G M, McCulloch M B, Yates W B, 1994. Crop classifica-tion from c-band polarimetric radar data. International Journal of Remote Sensing, 15(14):2871-2885. doi:10.1080/0143116 9408954289
[14]	Forkuor G, Conrad C, Thiel M et al., 2014. Integration of optical and synthetic aperture radar imagery for improving crop map-ping in Northwestern Benin, West Africa. Remote Sensing, 6(7):6472-6499. doi: 10.3390/rs6076472
[15]	Heller E, Rhemtulla J, Lele S et al., 2012. Mapping crop types, irrigated areas, and cropping intensities in heterogeneous land-scapes of southern india using multi-temporal medium-resolution imagery:implications for assessing water use in agriculture. Photogrammetric Engineering & Remote Sensing, 78(8):815-827. doi: 10.14358/PERS.78.8.815
[16]	Hütt C, Waldhoff G, 2018. Multi-data approach for crop classifi-cation using multitemporal, dual-polarimetric TerraSAR-X data, and official geodata. European Journal of Remote Sensing, 51(1):62-74. doi: 10.1080/22797254.2017.1401909
[17]	Jackson R D, 1984. Remote sensing of vegetation characteristics for farm management. In:Proceedings of SPIE 0475, Remote Sensing:Critical Review of Technology. Arlington:SPIE. doi: 10.1117/12.966243
[18]	Lee J S, Jurkevich I, Dewaele P et al. 1994, Speckle filtering of synthetic aperture aadar images:a review. Remote Sensing Reviews, 8(4):313-40. https://doi.org/10.1080/027572594095 32206
[19]	Le Toan T, Laur H, Mougin E et al., 1989. Multitemporal and dual-polarization observations of agricultural vegetation covers by X-band SAR images. IEEE Transactions on Geoscience and Remote Sensing, 27(6):709-718. doi:10.1109/TGRS. 1989.1398243
[20]	Lin Sen, Liu Ronggao, 2016. A simple method to extract tropical monsoon forests using NDVI based on MODIS data:a case study in South Asia and Peninsula Southeast Asia. Chinese Geographical Science, 26(1):22-34. doi: 10.1007/s11769-015-0789-3
[21]	Lopes A, Nezry E, Touzi R et al., 1993. Structure detection and statistical adaptive speckle filtering in SAR images. Interna-tional Journal of Remote Sensing, 14(9):1735-1758. doi: 10.1080/01431169308953999
[22]	Marongwe L, Kwazira M, Jenrich et al., 2011. An African success:the case of conservation agriculture in Zimbabwe. International Journal of Agricultural Sustainability, 9(1):153-161. doi: 10.3763/ijas.2010.0556
[23]	Martínez-Casasnovas J A, Martín-Montero A, 2003. Application of Landsat TM images to map long term cropping patterns. http://www.macaulay.ac.uk/workshop/remotesensing2004/JAMC_Full_paper.pdf. Cited 24 March 2018
[24]	Mattia F, Satalino G, Balenzano A et al., 2015. Sentinel-1 for wheat mapping and soil moisture retrieval. Proceedings of 2015 IEEE International Geoscience and Remote Sensing Symposium. Milan, Italy:IEEE, 2832-2835. doi: 10.1109/IGARSS.2015.7326404
[25]	McNairn H, Shang J L, 2016. A review of multitemporal synthetic aperture radar (SAR) for crop monitoring. In:Ban Y F (ed). Multitemporal Remote Sensing:Methods and Applications. Cham:Springer, 317-340. doi: 10.1007/978-3-319-47037-5_15
[26]	Moran S M, Alonso L, Moreno J F et al., 2012. A RADARSAT-2 quad-polarized time series for monitoring crop and soil condi-tions in Barrax, Spain. IEEE Transactions on Geoscience and Remote Sensing, 50(4):1057-1070. doi:10.1109/TGRS.2011. 2166080
[27]	Mukwada G, Manatsa D, 2013. Geospatial and temporal analysis of drought years in Zimbabwe, 1940-1999. Geographia Po-lonica, 86(4):313-326. doi: 10.7163/GPol.2013.26
[28]	Nagraj G M, Karegowda A G, 2016. Crop mapping using SAR imagery:an review. International Journal of Advanced Re-search in Computer Science, 7(7):47-52.
[29]	Nguyen D B, Clauss K, Cao S M et al., 2015. Mapping rice sea-sonality in the mekong delta with multi-year envisat ASAR WSM data. Remote Sensing, 7(12):15868-15893. doi:10. 3390/rs71215808
[30]	Nyoungui E, Tonye E, Akono A, 2002. Evaluation of Speckle Filtering and Texture Analysis Methods for Land Cover Clas-sification from SAR Images. International Journal of Remote Sensing, 23(9):1895-1925. doi: 10.1080/01431160110036157
[31]	Ozdarici A, Akyurek A, 2010. A Comparison of SAR Filtering Techniques on Agricultural Area Identification. In:ASPRS 2010 Annual Conference. San Diego, USA. http://info.asprs.org/publications/proceedings/sandiego2010/sandiego10/Ozdarici.pdf. Cited 17 May 2019
[32]	Portnoi M D, 2017. Methods for Sugarcane Harvest Detection Using Polarimetric SAR. Stellenbosch:Stellenbosch University.
[33]	Satalino G, Mattia F, Le Toan T et al., 2009. Wheat crop mapping by using ASAR AP data. IEEE Transactions on Geoscience and Remote Sensing, 47(2):527-530. doi:10.1109/TGRS. 2008.2008026
[34]	Sharma M P, Yadav M, Prawasi R et al., 2011. Cropping system analysis using remote sensing and GIS:a block level study of kurukshetra district. ARPN Journal of Agricultural and Bio-logical Science, 6(10):45-51.
[35]	Suresh G, Gehrke R, Wiatr T et al., 2016. Synthetic aperture radar (SAR) based classifiers for land applications in germany. Pro-ceedings of International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Prague, Czech Republic:ISPRS, 1187-1193. doi: 10.5194/isprs-archives-XLI-B1-1187-2016
[36]	Toringepi G, 2016. The Contribution of Smallholder Agriculture Production to Food Security in Rural Zimbabwe:A Case Study of Masvingo Province. South Africa:University of Fort Hare
[37]	Veci L, 2016. Sentinel-1 toolbox:TOPS interferometry tutorial. https://step.esa.int/docs/tutorials/S1TBX%20TOPSAR%20Interferometry%20with%20Sentinel-1%20Tutorial.pdf. Cited 20 March 2018
[38]	Wang Dan, Lin Hui, Chen Jinsong et al., 2010. Application of multi-temporal ENVISAT ASAR data to agricultural area mapping in the pearl river delta. International Journal of Re-mote Sensing, 31(6):1555-1572. doi:10.1080/01431160903 475258
[39]	Wegmüller U, Werner A, Wiesmann A et al., 2016. Time-series analysis of sentinel-1 interferometric wide swath data:tech-niques and challenges. Proceedings of 2016 IEEE International Geoscience and Remote Sensing Symposium. Beijing, China:IEEE, 3898-3901. doi: 10.1109/IGARSS.2016.7730012
[40]	Yan Huimin, Xiao Xiangming, Huang Heqing et al., 2014. Mul-tiple cropping intensity in China derived from agro-mete-orological observations and MODIS data. Chinese Geographical Science, 24(2):205-219. doi: 10.1007/s11769-013-0637-2

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通讯作者: 陈斌, bchen63@163.com

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沈阳化工大学材料科学与工程学院沈阳 110142

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Exploring the Potential of Mapping Cropping Patterns on Smallholder Scale Croplands Using Sentinel-1 SAR Data

College of GeoExploration Science and Information Engineering, Jilin University, Changchun 130026, China

Funds: Under the auspices of Fundamental Research Funds for the Central Universities, China (No. 2017TD-26), the Plan for Changbai Mountain Scholars of Jilin Province, China (No. JJLZ[2015]54)

Corresponding author: Juliana USEYA.E-mail:julieuseya@yahoo.co.uk;CHEN Shengbo.E-mail:chensb@jlu.edu.cn

Received Date: 2018-12-03

Rev Recd Date: 2018-08-08

Publish Date: 2019-08-01

Key words:

Abstract: It is of paramount importance to have sustainable agriculture since agriculture is the backbone of many nations' economic development. Majority of agricultural professionals rarely capture the cropping patterns necessary to promote Good Agricultural Practises. Objective of this research is to explore the potential of mapping cropping patterns occurring on different field parcels on small-scale farmlands in Zimbabwe. The first study location under investigation are the International Maize and Wheat Improvement Center (CIMMYT) research station and a few neighboring fields, the second is Middle Sabi Estate. Fourier time series modeling was implemented to determine the trends befalling on the two study sites. Results reveal that Sentinel-1 synthetic aperture radar (SAR) time series allow detection of subtle changes that occur to the crops and fields respectively, hence can be utilized to detect cropping patterns on small-scale farmlands. Discrimination of the main crops (maize and soybean) grown at CIMMYT was possible, and crop rotation was synthesized where sowing starts in November. A single cropping of early and late crops was observed, there were no winter crops planted during the investigation period. At Middle Sabi Estate, single cropping on perennial sugarcane fields and triple cropping of fields growing leafy vegetables, tomatoes and onions were observed. Classification of stacked images was used to derive the crop rotation maps representing what is practised at the farming lands. Random forest classification of the multi-temporal image stacks achieved overall accuracies of 99% and 95% on the respective study sites. In conclusion, Sentinel-1 time series can be implemented effectively to map the cropping patterns and crop rotations occurring on small-scale farming land. We recommend the use of Sentinel-1 SAR multi-temporal data to spatially explicitly map cropping patterns of single-, double- and triple-cropping systems on both small-scale and large-scale farming areas to ensure food security.

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Reference (40)

[1]	Ban Y, Howarth P J, 1999. Multitemporal ERS-1 SAR data for crop classification:a sequential-masking approach. Canadian Journal of Remote Sensing, 25(5):438-447. doi:10.1080/07038992.1999.10874743
[2]	Bargiel D, 2017. A new method for crop classification combining time series of radar images and crop phenology information. Remote Sensing of Environment, 198:369-383. doi:10.1016/j.rse.2017.06.022
[3]	Bégué A, Arvor D, Bellon B et al., 2018. Remote sensing and cropping practices:a review. Remote Sensing, 10(1):99. doi:10.3390/rs10010099
[4]	Bharati P, De U K, Pal M, 2015. A modified diversity index and its application to crop diversity in Assam, India. AIP Conference Proceedings, 1643(1):19-29. doi:10.1063/1.4907421
[5]	Breiman L, 2001. Random forest. Machine Learning, 45(1):5-32. doi:10.1109/ACCESS.2019.2912807.
[6]	Chamundeeswari V V, Singh D, Singh K, 2007. Unsupervised land cover classification of SAR images by contour tracing. In:Proceedings of 2007 IEEE International Geoscience and Remote Sensing Symposium. Barcelona, Spain:IEEE, 547-550. doi:10.1109/IGARSS.2007.4422852
[7]	CIMMYT, 2016. Our work. http://www.cimmyt.org/our-work/. Cited 22 March 2018.
[8]	Crawford M M, Ricard M R, 1998. Hierarchical classification of SAR data using a markov random field model. In:Proceedings of 1998 IEEE Southwest Symposium on Image Analysis and Interpretation. Tucson, AZ, USA:IEEE. doi:10.1109/IAI.1998.666864
[9]	de Oliveira A T C, de Oliveira L T, de Carvalho L M T et al., 2009. Separabilities of forest types in amplitude-phase space of multi-temporal MODIS NDVI. In:Proceedings of the Anais 14th Simpósio Brasileiro de Sensoriamento Remoto. Natal, Brasil:INPE, 7.
[10]	Dimov D, Kuhn J, Conrad C, 2016. Assessment of cropping sys-tem diversity in the fergana valley through image fusion of landsat 8 and sentinel-1. Proceedings of ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Prague, Czech Republic:ISPRS, 173-180. doi:10.5194/isprsannals-Ⅲ-7-173-2016
[11]	Doukkali F, 2017. Clustering using K-means algorithm. Towards Data Science. https://towardsdatascience.com/clustering-using-k-means-algorithm-81da00f156f6?gi=c1eec7743ec0. Cited 20 March 2018
[12]	Dzirutwe M, 2015. Zimbabwe takes tobacco road to agriculture recovery. https://www.yahoo.com/news/zimbabwe-takes-to-bacco-road-agriculture-recovery-082633369-business.html. Cited 21 March 2018
[13]	Foody G M, McCulloch M B, Yates W B, 1994. Crop classifica-tion from c-band polarimetric radar data. International Journal of Remote Sensing, 15(14):2871-2885. doi:10.1080/0143116 9408954289
[14]	Forkuor G, Conrad C, Thiel M et al., 2014. Integration of optical and synthetic aperture radar imagery for improving crop map-ping in Northwestern Benin, West Africa. Remote Sensing, 6(7):6472-6499. doi:10.3390/rs6076472
[15]	Heller E, Rhemtulla J, Lele S et al., 2012. Mapping crop types, irrigated areas, and cropping intensities in heterogeneous land-scapes of southern india using multi-temporal medium-resolution imagery:implications for assessing water use in agriculture. Photogrammetric Engineering & Remote Sensing, 78(8):815-827. doi:10.14358/PERS.78.8.815
[16]	Hütt C, Waldhoff G, 2018. Multi-data approach for crop classifi-cation using multitemporal, dual-polarimetric TerraSAR-X data, and official geodata. European Journal of Remote Sensing, 51(1):62-74. doi:10.1080/22797254.2017.1401909
[17]	Jackson R D, 1984. Remote sensing of vegetation characteristics for farm management. In:Proceedings of SPIE 0475, Remote Sensing:Critical Review of Technology. Arlington:SPIE. doi:10.1117/12.966243
[18]	Lee J S, Jurkevich I, Dewaele P et al. 1994, Speckle filtering of synthetic aperture aadar images:a review. Remote Sensing Reviews, 8(4):313-40. https://doi.org/10.1080/027572594095 32206
[19]	Le Toan T, Laur H, Mougin E et al., 1989. Multitemporal and dual-polarization observations of agricultural vegetation covers by X-band SAR images. IEEE Transactions on Geoscience and Remote Sensing, 27(6):709-718. doi:10.1109/TGRS. 1989.1398243
[20]	Lin Sen, Liu Ronggao, 2016. A simple method to extract tropical monsoon forests using NDVI based on MODIS data:a case study in South Asia and Peninsula Southeast Asia. Chinese Geographical Science, 26(1):22-34. doi:10.1007/s11769-015-0789-3
[21]	Lopes A, Nezry E, Touzi R et al., 1993. Structure detection and statistical adaptive speckle filtering in SAR images. Interna-tional Journal of Remote Sensing, 14(9):1735-1758. doi:10.1080/01431169308953999
[22]	Marongwe L, Kwazira M, Jenrich et al., 2011. An African success:the case of conservation agriculture in Zimbabwe. International Journal of Agricultural Sustainability, 9(1):153-161. doi:10.3763/ijas.2010.0556
[23]	Martínez-Casasnovas J A, Martín-Montero A, 2003. Application of Landsat TM images to map long term cropping patterns. http://www.macaulay.ac.uk/workshop/remotesensing2004/JAMC_Full_paper.pdf. Cited 24 March 2018
[24]	Mattia F, Satalino G, Balenzano A et al., 2015. Sentinel-1 for wheat mapping and soil moisture retrieval. Proceedings of 2015 IEEE International Geoscience and Remote Sensing Symposium. Milan, Italy:IEEE, 2832-2835. doi:10.1109/IGARSS.2015.7326404
[25]	McNairn H, Shang J L, 2016. A review of multitemporal synthetic aperture radar (SAR) for crop monitoring. In:Ban Y F (ed). Multitemporal Remote Sensing:Methods and Applications. Cham:Springer, 317-340. doi:10.1007/978-3-319-47037-5_15
[26]	Moran S M, Alonso L, Moreno J F et al., 2012. A RADARSAT-2 quad-polarized time series for monitoring crop and soil condi-tions in Barrax, Spain. IEEE Transactions on Geoscience and Remote Sensing, 50(4):1057-1070. doi:10.1109/TGRS.2011. 2166080
[27]	Mukwada G, Manatsa D, 2013. Geospatial and temporal analysis of drought years in Zimbabwe, 1940-1999. Geographia Po-lonica, 86(4):313-326. doi:10.7163/GPol.2013.26
[28]	Nagraj G M, Karegowda A G, 2016. Crop mapping using SAR imagery:an review. International Journal of Advanced Re-search in Computer Science, 7(7):47-52.
[29]	Nguyen D B, Clauss K, Cao S M et al., 2015. Mapping rice sea-sonality in the mekong delta with multi-year envisat ASAR WSM data. Remote Sensing, 7(12):15868-15893. doi:10. 3390/rs71215808
[30]	Nyoungui E, Tonye E, Akono A, 2002. Evaluation of Speckle Filtering and Texture Analysis Methods for Land Cover Clas-sification from SAR Images. International Journal of Remote Sensing, 23(9):1895-1925. doi:10.1080/01431160110036157
[31]	Ozdarici A, Akyurek A, 2010. A Comparison of SAR Filtering Techniques on Agricultural Area Identification. In:ASPRS 2010 Annual Conference. San Diego, USA. http://info.asprs.org/publications/proceedings/sandiego2010/sandiego10/Ozdarici.pdf. Cited 17 May 2019
[32]	Portnoi M D, 2017. Methods for Sugarcane Harvest Detection Using Polarimetric SAR. Stellenbosch:Stellenbosch University.
[33]	Satalino G, Mattia F, Le Toan T et al., 2009. Wheat crop mapping by using ASAR AP data. IEEE Transactions on Geoscience and Remote Sensing, 47(2):527-530. doi:10.1109/TGRS. 2008.2008026
[34]	Sharma M P, Yadav M, Prawasi R et al., 2011. Cropping system analysis using remote sensing and GIS:a block level study of kurukshetra district. ARPN Journal of Agricultural and Bio-logical Science, 6(10):45-51.
[35]	Suresh G, Gehrke R, Wiatr T et al., 2016. Synthetic aperture radar (SAR) based classifiers for land applications in germany. Pro-ceedings of International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Prague, Czech Republic:ISPRS, 1187-1193. doi:10.5194/isprs-archives-XLI-B1-1187-2016
[36]	Toringepi G, 2016. The Contribution of Smallholder Agriculture Production to Food Security in Rural Zimbabwe:A Case Study of Masvingo Province. South Africa:University of Fort Hare
[37]	Veci L, 2016. Sentinel-1 toolbox:TOPS interferometry tutorial. https://step.esa.int/docs/tutorials/S1TBX%20TOPSAR%20Interferometry%20with%20Sentinel-1%20Tutorial.pdf. Cited 20 March 2018
[38]	Wang Dan, Lin Hui, Chen Jinsong et al., 2010. Application of multi-temporal ENVISAT ASAR data to agricultural area mapping in the pearl river delta. International Journal of Re-mote Sensing, 31(6):1555-1572. doi:10.1080/01431160903 475258
[39]	Wegmüller U, Werner A, Wiesmann A et al., 2016. Time-series analysis of sentinel-1 interferometric wide swath data:tech-niques and challenges. Proceedings of 2016 IEEE International Geoscience and Remote Sensing Symposium. Beijing, China:IEEE, 3898-3901. doi:10.1109/IGARSS.2016.7730012
[40]	Yan Huimin, Xiao Xiangming, Huang Heqing et al., 2014. Mul-tiple cropping intensity in China derived from agro-mete-orological observations and MODIS data. Chinese Geographical Science, 24(2):205-219. doi:10.1007/s11769-013-0637-2

Exploring the Potential of Mapping Cropping Patterns on Smallholder Scale Croplands Using Sentinel-1 SAR Data

doi: 10.1007/s11769-019-1060-0

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